# Feature Request: Metabolic Profile – FatMax and Fuel Utilization

**URL:** <https://forum.intervals.icu/t/feature-request-metabolic-profile-fatmax-and-fuel-utilization/101309>\
**Category:** Feature Requests\
**Created:** [18 May 2025 13:03 UTC](https://forum.intervals.icu/t/feature-request-metabolic-profile-fatmax-and-fuel-utilization/101309 "2025-05-18T13:03:06Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![Andreas\_Schnederle-W](https://forum.intervals.icu/user_avatar/forum.intervals.icu/andreas_schnederle-w/32/37514_2.png) [@Andreas\_Schnederle-W](https://forum.intervals.icu/u/Andreas_Schnederle-W)\
**Post date:** [18 May 2025 13:11 UTC](https://forum.intervals.icu/t/feature-request-metabolic-profile-fatmax-and-fuel-utilization/101309/2 "2025-05-18T13:11:59Z")

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Just to add to my original suggestion:

While precise values for FatMax, CHO/Fat oxidation, or metabolic crossover points **do require lab-based metabolic testing** , there is **increasing scientific support and practical application** for estimating these values using field data — especially from power meters, heart rate monitors, and established physiological models.

Here are some relevant **research insights and modeling approaches** :

* * *

### 🔬 Scientific Background & Modeling Examples

**1. Estimating FatMax and Fuel Usage from HR and Power Data**  
Several models and studies suggest it’s possible to **estimate fat vs. CHO utilization** using HR, power output, and zone-based intensity.

- **Maunder et al. (2018)**  
“Fat oxidation during exercise: determinants and constraints”  
➤ Shows that FatMax typically occurs between 45–65% of VO₂max — which can be inferred from threshold models or zone estimates.  
🔗 [Frontiers in Physiology](https://www.frontiersin.org/articles/10.3389/fphys.2018.00599/full)

- **Venables et al. (2005)**  
“Determinants of fat oxidation during exercise in healthy men and women”  
➤ Demonstrates that fat oxidation rates are largely predictable from VO₂max and exercise intensity.  
🔗 [PubMed](https://pubmed.ncbi.nlm.nih.gov/15333616/)

* * *

**2. Fuel Estimation from Power Data Alone**  
Some tools (e.g., **Xert** , **GoldenCheetah** , **WKO** ) use power-duration relationships to **estimate substrate usage** :

- Caloric burn = Power × Gross Efficiency (~22–25%)
- Then, based on intensity relative to threshold, %CHO vs. %Fat can be estimated (e.g., higher CHO above LT1/LT2)

* * *

**3. VO₂max / VLamax Based Models (e.g. INSCYD, Aerotune, Sentiero)**  
These platforms estimate substrate utilization via field testing:

- VLamax (glycolytic rate) + VO₂max are used to model the balance between aerobic (fat-dominant) and anaerobic (CHO-heavy) energy systems.
- Metrics like **FatMax** , **CHO burn (g/h)**, **Fat burn (g/h)** across wattage are derived using athlete profiles and power data — no lab required.

Example from INSCYD:  
🔗 [Understanding VLamax & FatMax](https://inscyd.com/article/anaerobic-threshold-4mmol-lactate/)

* * *

### 💡 Conclusion

So while **individual accuracy will always benefit from lab diagnostics** , there is a strong case for implementing **data-driven estimates** of metabolic profiles in platforms like Intervals.icu — especially considering how much high-quality power and HR data is already available.

This could enable users to:

- Identify their estimated FatMax zone
- Visualize substrate usage across intensities
- Tailor training and fueling strategies accordingly

Would love to hear if others are interested in this too, or have worked on similar implementations!

**P.S.** Since _CHO Used_ is already integrated into Intervals.icu, this feature could potentially build on existing data structures — making it even more feasible to add fuel utilization curves and FatMax estimations. ([Carb utilisation and ingestion on activities](https://forum.intervals.icu/t/carb-utilisation-and-ingestion-on-activities/78676))

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